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test_pipelines_conversational.py
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test_pipelines_conversational.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import (
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
Conversation,
ConversationalPipeline,
is_torch_available,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_torch, slow, torch_device
from .test_pipelines_common import MonoInputPipelineCommonMixin
if is_torch_available():
import torch
from transformers.models.gpt2 import GPT2Config, GPT2LMHeadModel
DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
@is_pipeline_test
class SimpleConversationPipelineTests(unittest.TestCase):
def get_pipeline(self):
# When
config = GPT2Config(
vocab_size=263,
n_ctx=128,
max_length=128,
n_embd=64,
n_layer=1,
n_head=8,
bos_token_id=256,
eos_token_id=257,
)
model = GPT2LMHeadModel(config)
# Force model output to be L
V, D = model.lm_head.weight.shape
bias = torch.zeros(V)
bias[76] = 1
weight = torch.zeros((V, D), requires_grad=True)
model.lm_head.bias = torch.nn.Parameter(bias)
model.lm_head.weight = torch.nn.Parameter(weight)
# # Created with:
# import tempfile
# from tokenizers import Tokenizer, models
# from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
# vocab = [(chr(i), i) for i in range(256)]
# tokenizer = Tokenizer(models.Unigram(vocab))
# with tempfile.NamedTemporaryFile() as f:
# tokenizer.save(f.name)
# real_tokenizer = PreTrainedTokenizerFast(tokenizer_file=f.name, eos_token="<eos>", bos_token="<bos>")
# real_tokenizer._tokenizer.save("dummy.json")
# Special tokens are automatically added at load time.
tokenizer = AutoTokenizer.from_pretrained("Narsil/small_conversational_test")
conversation_agent = pipeline(
task="conversational", device=DEFAULT_DEVICE_NUM, model=model, tokenizer=tokenizer
)
return conversation_agent
@require_torch
def test_integration_torch_conversation(self):
conversation_agent = self.get_pipeline()
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
conversation_2 = Conversation("What's the last book you have read?")
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
result = conversation_agent([conversation_1, conversation_2], max_length=48)
# Two conversations in one pass
self.assertEqual(result, [conversation_1, conversation_2])
self.assertEqual(
result,
[
Conversation(
None,
past_user_inputs=["Going to the movies tonight - any suggestions?"],
generated_responses=["L"],
),
Conversation(
None, past_user_inputs=["What's the last book you have read?"], generated_responses=["L"]
),
],
)
# One conversation with history
conversation_2.add_user_input("Why do you recommend it?")
result = conversation_agent(conversation_2, max_length=64)
self.assertEqual(result, conversation_2)
self.assertEqual(
result,
Conversation(
None,
past_user_inputs=["What's the last book you have read?", "Why do you recommend it?"],
generated_responses=["L", "L"],
),
)
class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
pipeline_task = "conversational"
small_models = [] # Models tested without the @slow decorator
large_models = ["microsoft/DialoGPT-medium"] # Models tested with the @slow decorator
invalid_inputs = ["Hi there!", Conversation()]
def _test_pipeline(
self, nlp
): # override the default test method to check that the output is a `Conversation` object
self.assertIsNotNone(nlp)
# We need to recreate conversation for successive tests to pass as
# Conversation objects get *consumed* by the pipeline
conversation = Conversation("Hi there!")
mono_result = nlp(conversation)
self.assertIsInstance(mono_result, Conversation)
conversations = [Conversation("Hi there!"), Conversation("How are you?")]
multi_result = nlp(conversations)
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], Conversation)
# Conversation have been consumed and are not valid anymore
# Inactive conversations passed to the pipeline raise a ValueError
self.assertRaises(ValueError, nlp, conversation)
self.assertRaises(ValueError, nlp, conversations)
for bad_input in self.invalid_inputs:
self.assertRaises(Exception, nlp, bad_input)
self.assertRaises(Exception, nlp, self.invalid_inputs)
@require_torch
@slow
def test_integration_torch_conversation(self):
# When
nlp = pipeline(task="conversational", device=DEFAULT_DEVICE_NUM)
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
conversation_2 = Conversation("What's the last book you have read?")
# Then
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
# When
result = nlp([conversation_1, conversation_2], do_sample=False, max_length=1000)
# Then
self.assertEqual(result, [conversation_1, conversation_2])
self.assertEqual(len(result[0].past_user_inputs), 1)
self.assertEqual(len(result[1].past_user_inputs), 1)
self.assertEqual(len(result[0].generated_responses), 1)
self.assertEqual(len(result[1].generated_responses), 1)
self.assertEqual(result[0].past_user_inputs[0], "Going to the movies tonight - any suggestions?")
self.assertEqual(result[0].generated_responses[0], "The Big Lebowski")
self.assertEqual(result[1].past_user_inputs[0], "What's the last book you have read?")
self.assertEqual(result[1].generated_responses[0], "The Last Question")
# When
conversation_2.add_user_input("Why do you recommend it?")
result = nlp(conversation_2, do_sample=False, max_length=1000)
# Then
self.assertEqual(result, conversation_2)
self.assertEqual(len(result.past_user_inputs), 2)
self.assertEqual(len(result.generated_responses), 2)
self.assertEqual(result.past_user_inputs[1], "Why do you recommend it?")
self.assertEqual(result.generated_responses[1], "It's a good book.")
@require_torch
@slow
def test_integration_torch_conversation_truncated_history(self):
# When
nlp = pipeline(task="conversational", min_length_for_response=24, device=DEFAULT_DEVICE_NUM)
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
# Then
self.assertEqual(len(conversation_1.past_user_inputs), 0)
# When
result = nlp(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 1)
self.assertEqual(len(result.generated_responses), 1)
self.assertEqual(result.past_user_inputs[0], "Going to the movies tonight - any suggestions?")
self.assertEqual(result.generated_responses[0], "The Big Lebowski")
# When
conversation_1.add_user_input("Is it an action movie?")
result = nlp(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 2)
self.assertEqual(len(result.generated_responses), 2)
self.assertEqual(result.past_user_inputs[1], "Is it an action movie?")
self.assertEqual(result.generated_responses[1], "It's a comedy.")
@require_torch
@slow
def test_integration_torch_conversation_dialogpt_input_ids(self):
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
nlp = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
inputs = nlp._parse_and_tokenize([conversation_1])
self.assertEqual(inputs["input_ids"].tolist(), [[31373, 50256]])
conversation_2 = Conversation("how are you ?", past_user_inputs=["hello"], generated_responses=["Hi there!"])
inputs = nlp._parse_and_tokenize([conversation_2])
self.assertEqual(
inputs["input_ids"].tolist(), [[31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256]]
)
inputs = nlp._parse_and_tokenize([conversation_1, conversation_2])
self.assertEqual(
inputs["input_ids"].tolist(),
[
[31373, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256],
[31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256],
],
)
@require_torch
@slow
def test_integration_torch_conversation_blenderbot_400M_input_ids(self):
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
nlp = ConversationalPipeline(model=model, tokenizer=tokenizer)
# test1
conversation_1 = Conversation("hello")
inputs = nlp._parse_and_tokenize([conversation_1])
self.assertEqual(inputs["input_ids"].tolist(), [[1710, 86, 2]])
# test2
conversation_1 = Conversation(
"I like lasagne.",
past_user_inputs=["hello"],
generated_responses=[
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie."
],
)
inputs = nlp._parse_and_tokenize([conversation_1])
self.assertEqual(
inputs["input_ids"].tolist(),
[
# This should be compared with the same conversation on ParlAI `safe_interactive` demo.
[
1710, # hello
86,
228, # Double space
228,
946,
304,
398,
6881,
558,
964,
38,
452,
315,
265,
6252,
452,
322,
968,
6884,
3146,
278,
306,
265,
617,
87,
388,
75,
341,
286,
521,
21,
228, # Double space
228,
281, # I like lasagne.
398,
6881,
558,
964,
21,
2, # EOS
]
],
)
@require_torch
@slow
def test_integration_torch_conversation_blenderbot_400M(self):
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
nlp = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
result = nlp(
conversation_1,
)
self.assertEqual(
result.generated_responses[0],
# ParlAI implementation output, we have a different one, but it's our
# second best, you can check by using num_return_sequences=10
# " Hello! How are you? I'm just getting ready to go to work, how about you?",
" Hello! How are you doing today? I just got back from a walk with my dog.",
)
conversation_1 = Conversation("Lasagne hello")
result = nlp(conversation_1, encoder_no_repeat_ngram_size=3)
self.assertEqual(
result.generated_responses[0],
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie.",
)
conversation_1 = Conversation(
"Lasagne hello Lasagne is my favorite Italian dish. Do you like lasagne? I like lasagne."
)
result = nlp(
conversation_1,
encoder_no_repeat_ngram_size=3,
)
self.assertEqual(
result.generated_responses[0],
" Me too. I like how it can be topped with vegetables, meats, and condiments.",
)
@require_torch
@slow
def test_integration_torch_conversation_encoder_decoder(self):
# When
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot_small-90M")
nlp = ConversationalPipeline(model=model, tokenizer=tokenizer, device=DEFAULT_DEVICE_NUM)
conversation_1 = Conversation("My name is Sarah and I live in London")
conversation_2 = Conversation("Going to the movies tonight, What movie would you recommend? ")
# Then
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
# When
result = nlp([conversation_1, conversation_2], do_sample=False, max_length=1000)
# Then
self.assertEqual(result, [conversation_1, conversation_2])
self.assertEqual(len(result[0].past_user_inputs), 1)
self.assertEqual(len(result[1].past_user_inputs), 1)
self.assertEqual(len(result[0].generated_responses), 1)
self.assertEqual(len(result[1].generated_responses), 1)
self.assertEqual(result[0].past_user_inputs[0], "My name is Sarah and I live in London")
self.assertEqual(
result[0].generated_responses[0],
"hi sarah, i live in london as well. do you have any plans for the weekend?",
)
self.assertEqual(
result[1].past_user_inputs[0], "Going to the movies tonight, What movie would you recommend? "
)
self.assertEqual(
result[1].generated_responses[0], "i don't know... i'm not really sure. what movie are you going to see?"
)
# When
conversation_1.add_user_input("Not yet, what about you?")
conversation_2.add_user_input("What's your name?")
result = nlp([conversation_1, conversation_2], do_sample=False, max_length=1000)
# Then
self.assertEqual(result, [conversation_1, conversation_2])
self.assertEqual(len(result[0].past_user_inputs), 2)
self.assertEqual(len(result[1].past_user_inputs), 2)
self.assertEqual(len(result[0].generated_responses), 2)
self.assertEqual(len(result[1].generated_responses), 2)
self.assertEqual(result[0].past_user_inputs[1], "Not yet, what about you?")
self.assertEqual(result[0].generated_responses[1], "i don't have any plans yet. i'm not sure what to do yet.")
self.assertEqual(result[1].past_user_inputs[1], "What's your name?")
self.assertEqual(result[1].generated_responses[1], "i don't have a name, but i'm going to see a horror movie.")

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